T Cell Heterogeneity Shaped by Alternative Splicing Predicts Prognosis in Hepatocellular Carcinoma

Area of Science:

• Immunology
• Genomics
• Oncology

Background:

• Alternative splicing (AS) influences immune function and cellular diversity.
• The role of AS in hepatocellular carcinoma (HCC) T-cell behavior and outcomes is unclear.

Purpose of the Study:

• Investigate AS impact on T-cell heterogeneity in HCC.
• Develop an AS-based prognostic model for HCC patient outcomes.

Main Methods:

• Utilized single-cell full-length transcriptome data and the SCASL algorithm to identify AS events.
• Performed differential expression, functional enrichment, cell interaction, and RNA velocity analyses.
• Constructed and validated an AS event-based survival prognosis model.

Main Results:

• Identified two distinct T-cell subtypes based on AS profiles, separate from gene expression clustering.
• These T-cell subtypes showed enrichment in immune-related pathways (e.g., NOD-like receptors, T-cell receptor signaling).
• Developed a validated prognostic model distinguishing HCC patient risk levels based on AS events.

Conclusions:

• AS plays a critical role in T-cell functional heterogeneity within HCC.
• The AS-based prognostic model demonstrates clinical relevance for predicting patient outcomes and immunotherapy response.
• Findings offer insights into tumor-immune interactions and potential splicing-derived biomarkers.